refactor: simplification

popmon/analysis/comparison/hist_comparer.py

@@ -57,20 +57,20 @@ def hist_compare(row, hist_name1="", hist_name2="", max_res_bound=7.0):
         Default is 7.0.
     :return: pandas Series with popular comparison metrics.
     """
-    x = pd.Series()
-    x["ks"] = np.nan
-    x["ks_zscore"] = np.nan
-    x["ks_pvalue"] = np.nan
-    x["pearson"] = np.nan
-    x["chi2"] = np.nan
-    x["chi2_norm"] = np.nan
-    x["chi2_zscore"] = np.nan
-    x["chi2_pvalue"] = np.nan
-    x["chi2_max_residual"] = np.nan
-    x["chi2_spike_count"] = np.nan
-    x["max_prob_diff"] = np.nan
-    unknown_labels = np.nan
-    x["unknown_labels"] = unknown_labels
+    x = {
+        "ks": np.nan,
+        "ks_zscore": np.nan,
+        "ks_pvalue": np.nan,
+        "pearson": np.nan,
+        "chi2": np.nan,
+        "chi2_norm": np.nan,
+        "chi2_zscore": np.nan,
+        "chi2_pvalue": np.nan,
+        "chi2_max_residual": np.nan,
+        "chi2_spike_count": np.nan,
+        "max_prob_diff": np.nan,
+        "unknown_labels": np.nan,
+    }
 
     # basic name checks
     cols = row.index.to_list()
@@ -83,15 +83,14 @@ def hist_compare(row, hist_name1="", hist_name2="", max_res_bound=7.0):
     # basic histogram checks
     hist1 = row[hist_name1]
     hist2 = row[hist_name2]
-    if not all([isinstance(hist, COMMON_HIST_TYPES) for hist in [hist1, hist2]]):
-        return x
-    if not check_similar_hists([hist1, hist2]):
-        return x
+    if not all(
+        [isinstance(hist, COMMON_HIST_TYPES) for hist in [hist1, hist2]]
+    ) or not check_similar_hists([hist1, hist2]):
+        return pd.Series(x)
 
     # compare
-    is_num = is_numeric(hist1)
     if hist1.n_dim == 1:
-        if is_num:
+        if is_numeric(hist1):
             numpy_1dhists = get_consistent_numpy_1dhists([hist1, hist2])
             entries_list = [nphist[0] for nphist in numpy_1dhists]
             # KS-test only properly defined for (ordered) 1D interval variables
@@ -106,10 +105,14 @@ def hist_compare(row, hist_name1="", hist_name2="", max_res_bound=7.0):
             labels1 = hist1.bin_labels()
             labels2 = hist2.bin_labels()
             subset = set(labels1) <= set(labels2)
-            unknown_labels = int(not subset)
+            x["unknown_labels"] = int(not subset)
     elif hist1.n_dim == 2:
         numpy_2dgrids = get_consistent_numpy_2dgrids([hist1, hist2])
         entries_list = [entry.flatten() for entry in numpy_2dgrids]
+    else:
+        raise NotImplementedError(
+            f"histogram with dimension {hist1.n_dim} is not supported"
+        )
 
     # calculate pearson coefficient
     pearson, pvalue = (np.nan, np.nan)
@@ -130,8 +133,7 @@ def hist_compare(row, hist_name1="", hist_name2="", max_res_bound=7.0):
     x["chi2_max_residual"] = max(list(map(abs, res)))
     x["chi2_spike_count"] = sum(abs(r) > max_res_bound for r in res)
     x["max_prob_diff"] = googl_test(*entries_list)
-    x["unknown_labels"] = unknown_labels
-    return x
+    return pd.Series(x)
 
 
 class HistComparer(Pipeline):

popmon/analysis/profiling/hist_profiler.py

@@ -148,7 +148,7 @@ def _profile_1d_histogram(self, name, hist):
                         for f_name, result in zip(name, results)
                     ]
 
-                profile.update({k: v for k, v in zip(names, results)})
+                profile.update(dict(zip(names, results)))
         elif not is_num:
             profile["fraction_true"] = pm_np.fraction_of_true(bin_labels, bin_counts)
 
@@ -190,7 +190,6 @@ def _profile_hist(self, split, hist_name):
         is_num = is_numeric(hist0)
 
         # these are the profiled quantities we will monitor
-        fields = []
         if dimension == 1:
             fields = list(self.general_stats_1d)
             fields += (
@@ -200,6 +199,8 @@ def _profile_hist(self, split, hist_name):
             )
         elif dimension == 2:
             fields = list(self.general_stats_2d)
+        else:
+            fields = []
 
         # now loop over split-axis, e.g. time index, and profile each sub-hist x:y
         profile_list = []

popmon/pipeline/metrics.py

@@ -147,8 +147,8 @@ def stability_metrics(
         "monitoring_rules": monitoring_rules,
         "pull_rules": pull_rules,
         "features": features,
+        **kwargs,
     }
-    cfg.update(kwargs)
 
     datastore = {"hists": hists}
     if reference_type == "external":